I have more than one image open, and I'd like to run scrip-fu functions on the currently selected image. How can I get the image reference number to the currently selected image so I can use the reference in other functions (e.g. (gimp-image-width 7))? I'm using GIMP 2.10.12.
Using (gimp-image-list) lists all the image references:
> (gimp-image-list)
(12 #(12 11 10 9 8 7 6 5 4 3 2 1))
I've been using (gimp-image-latest) for this, which should work for the general case
(define (gimp-image-list-items)
(cadr (gimp-image-list)))
(define (gimp-image-latest)
(aref (gimp-image-list-items) 0))
(define img (gimp-image-latest))
Then it's in img. E.g.:
> (gimp-image-get-layers img)
(283 #(770 769 768 767 766 765 764 763 762 761 760 759 758 757 756 755 754 753 752 751 750 749 748 747 746 745 744 743 742 741 740 739 738 737 736 735 734 733 732 731 730 729 728 727 726 725 724 723 722 721 720 719 718 717 716 715 714 713 712 711 710 709 708 707 706 705 704 703 702 701 700 699 698 697 696 695 694 693 692 691 690 689 688 687 686 685 684 683 682 681 680 679 678 677 676 675 674 673 672 671 670 669 668 667 666 665 664 663 662 661 660 659 658 657 656 655 654 653 652 651 650 649 648 647 646 645 644 643 642 641 640 639 638 637 636 635 634 633 632 631 630 629 628 627 626 625 624 623 622 621 620 619 618 617 616 615 614 613 612 611 610 609 608 607 606 605 604 603 602 601 600 599 598 597 596 595 594 593 592 591 590 589 588 587 586 585 584 583 582 581 580 579 578 577 576 575 574 573 572 571 570 569 568 567 566 565 564 563 562 561 560 559 558 557 556 555 554 553 552 551 550 549 548 547 546 545 544 543 542 541 540 539 538 537 536 535 534 533 532 531 530 529 528 527 526 525 524 523 522 521 520 519 518 517 516 515 514 513 512 511 510 509 508 507 506 505 504 503 502 501 500 499 498 497 496 495 494 493 492 491 490 489 488))
Related
I've been trying to decode my IR raw data but always getting unknown code. i used
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#define SHORT 600
#define LONG 300
#define MARGIN 200
#define INTRO 3500
#define INTRO2 1700
#define SEPARATOR 9880
char decode(int val1, int val2){
if (abs(val1 - INTRO) < MARGIN && abs(val2 -
INTRO2) < MARGIN)
return '[';
if (abs(val1 - SHORT) < MARGIN && abs(val2 -
SHORT) < MARGIN)
return '0';
if (abs(val1 - SHORT) < MARGIN && abs(val2 -
LONG) < MARGIN)
return '1';
if (abs(val1 - SHORT) < MARGIN && abs(val2 -
SEPARATOR) < MARGIN)
return ']';
}
int abs(int val){
if (val < 0)
return -val;
else
return val;
}
int main(int argc, char** argv){
if (argc < 3){
printf("usage = %s fileName output\n",
argv[0]);
exit(0);
}
int val1, val2;
char delims[] = " ";
char *result = NULL;
FILE* f = fopen(argv[1],"r");
FILE* out = fopen(argv[2],"w");
char line[256];
int index = 0;
char* nIndex = 0;
while (fgets(line, 256, f) != NULL){
result = strtok( line, delims );
index = 0;
while( result != NULL ) {
//printf( "result[%d] is \"%s\"\n",
index, result );
if (index & 1){
if (nIndex = strchr(result,'\n')){
//printf("New line detected in result\n");
*nIndex = '\0';
}
val2 = atoi(result);
char dec = decode(val1,val2);
fprintf(out,"%c",dec);
}else{
val1 = atoi(result);
}
result = strtok( NULL, delims );
index++;
}
//printf (line);
}
fprintf(out, "]\n");
fclose(f);
fclose(out);
}
and my Raw data are
3596 1598 574 298 574 1166
572 301 603 268 576 295
573 298 600 271 610 260
577 295 572 299 573 298
573 299 603 268 577 1164
574 298 574 296 573 299
543 329 575 295 571 301
573 298 569 1173 576 1165
546 1197 662 184 636 261
544 1198 600 271 573 299
573 297 574 297 544 328
605 266 573 299 544 326
573 298 605 266 575 296
576 296 543 327 575 296
604 268 575 297 515 356
599 272 572 298 601 271
571 299 574 301 573 295
576 295 573 279 561 329
545 325 546 325 573 300
573 297 573 1169 572 1170
574 298 544 327 575 296
547 325 511 359 603 76167
3599 1625 548 322 553 1189
552 321 523 348 550 321
551 320 517 354 574 297
551 320 551 321 577 293
547 325 550 321 521 1219
525 348 552 319 550 321
549 322 552 320 549 322
550 321 548 1193 551 1191
584 1159 518 353 549 322
580 1170 546 317 551 321
552 319 579 292 551 320
552 320 551 320 553 318
549 322 579 292 547 324
550 321 555 317 550 320
581 1160 587 286 543 1198
552 1190 552 1191 550 321
551 321 581 290 547 324
549 322 550 321 548 1193
552 1191 550 319 583 292
550 320 576 295 579 292
551 320 519 353 551 319
548 323 553 1189 522 1220
550 1192 550 1192 551 1192
523 1219 551 1191 451 1291
522 350 550 321 553 318
548 323 555 316 551 320
553 319 552 319 554 316
584 300 566 294 550 320
552 319 549 322 550 321
553 321 552 317 549 321
550 1192 523 1219 550 1192
558 314 549 322 552 319
551 321 552 318 552 320
551 320 549 322 551 320
550 1192 548 1194 550 1192
551 320 613 258 526 346
554 319 549 322 522 348
576 294 522 350 522 348
555 317 523 348 552 319
552 319 550 321 548 323
552 319 580 1161 551 322
553 318 551 320 549 322
608 263 582 289 525 1216
550 322 521 350 552 320
551 320 552 319 549 322
581 290 551 321 550 320
552 319 555 317 523 348
548 324 551 319 546 325
551 321 479 391 572 1169
551 1192 554 1188 555 1188
551 321 549 1191 557 1187
551
and i got
[1�11111111111�1111111��#11#111111111111111111111111111111��11111�[1
11111111111+1111111##�11�11111111111111�1###111111##111111111
,###+#s111111111111111111#+#111111111###1111111111111111�111111(11111111111111111�#
please help me, thank you!
I am not used to playing with IR, but ...
If you want to use lirc you should configure it : http://lirc.org/html/configuration-guide.html.
You do not need to develop a specific utility to decode data.
You can use mode2 to get output form driver http://www.lirc.org/html/mode2.html
mode2 --driver default --device /dev/lirc0
Using driver default on device /dev/lirc0
Trying device: /dev/lirc0
Using device: /dev/lirc0
pulse 2750
space 800
pulse 500
space 350
pulse 550
space 350
pulse 550
Then lircd.conf is used to map data to key http://www.lirc.org/html/lircd.conf.html and with irw you get the result :
$ irw
000000037ff07bef 00 KEY_VOLUMEUP Acer_Aspire_6530G_MCE
000000037ff07bef 01 KEY_VOLUMEUP Acer_Aspire_6530G_MCE
000000037ff07bdd 00 KEY_ENTER Acer_Aspire_6530G_MCE
000000037ff07bdd 02 KEY_ENTER Acer_Aspire_6530G_MCE
It looks like you may be reading a remote intended for a Mitsubishi air conditioning system. Unlike most remotes, which just send a short signal corresponding to the button pressed and rely on the device being controlled to remember its previous state, Mitsubishi decided to transmit the entire desired state of the machine -- all the mode, fan, vane, and other settings get sent Every Time, as an 18-byte data block with checksum (which the remote then repeats once, as insurance against optical noise).
If you websearch "Mitsubishi minisplit IR protocol", you'll find some pages where people have at least partially analyzed this protocol.
This isn't the kind of approach LIRC's higher-level tools were designed to handle. You can still use the LIRC drivers, but they have to be accessed in "raw" mode and separate tools are used to interpret incoming signal or generate outgoing signal.
Searching Github finds libraries/tools others have written to work with this protocol. I haven't found one in a language I actually like working with, though, so I'm assuming I'm going to have to access the LIRC api calls to read/write the data and interpret/generate it myself.
RELATED: If you're using the ir-ctl program to do basic "raw" reading and writing while you test your hardware, be aware that ir-ctl's transmit mode has a line-length limit which this protocol sometimes exceeds if you put it all on a single line of text. The workaround is to insert line breaks every so often, so no single line of the input exceeds that hardcoded limit. (Yes, I've filed a bug report.)
I am writing my first report in RMarkdown and struggling with specific figure alignments.
I have some data that I am manipulating into a format friendly for the package pheatmap such that it produces heatmap HTML output. The code that produces one of these looks like:
cleaned_mayo<- cleaned_mayo[which(cleaned_mayo$Source=="MayoBrainBank_Dickson"),]
# Segregate data
ad<- cleaned_mayo[which(cleaned_mayo$Diagnosis== "AD"),-c(1:13)]
control<- cleaned_mayo[which(cleaned_mayo$Diagnosis== "Control"),-c(1:13)]
# Average data across patients and assign diagnoses
ad<- as.data.frame(t(apply(ad,2, mean)))
control<- as.data.frame(t(apply(control,2, mean)))
ad$Diagnosis<- "AD"
control$Diagnosis<- "Control"
# Combine
avg_heat<- rbind(ad, control)
# Rearrange columns
avg_heat<- avg_heat[,c(32, 1:31)]
# Mean shift all expression values
avg_heat[,2:32]<- apply(avg_heat[,2:32], 2, function(x){x-mean(x)})
#################################
# CREATE HEAT MAP
#################################
# Plot average heat map
pheatmap(t(avg_heat[,2:32]), cluster_col= F, labels_col= c("AD", "Control"),gaps_col = c(1), labels_row = colnames(avg_heat)[2:32],
main= "Mayo Differential Expression for Genes of Interest: Averaged Across \n Patients within a Diagnosis",
show_colnames = T)
Where the numeric columns of cleaned_mayo look like:
C1QA C1QC C1QB LAPTM5 CTSS FCER1G PLEK CSF1R CD74 LY86 AIF1 FGD2 TREM2 PTK2B LYN UNC93B1 CTSC NCKAP1L TMEM119 ALOX5AP LCP1
1924_TCX 1101 1392 1687 1380 380 279 198 1889 6286 127 252 771 338 5795 409 494 337 352 476 170 441
1926_TCX 881 770 950 1064 239 130 132 1241 3188 76 137 434 212 5634 327 419 292 217 464 124 373
1935_TCX 3636 4106 5196 5206 1226 583 476 5588 27650 384 1139 1086 756 14219 1269 869 868 1378 1270 428 1216
1925_TCX 3050 4392 5357 3585 788 472 350 4662 11811 340 865 1051 468 13446 638 420 1047 850 756 616 1008
1963_TCX 3169 2874 4182 2737 828 551 208 2560 10103 204 719 585 499 9158 546 335 598 593 606 418 707
7098_TCX 1354 1803 2369 2134 634 354 245 1829 8322 227 593 371 411 10637 504 294 750 458 367 490 779
ITGAM LPCAT2 LGALS9 GRN MAN2B1 TYROBP CD37 LAIR1 CTSZ CYTH4
1924_TCX 376 649 699 1605 618 392 328 628 1774 484
1926_TCX 225 381 473 1444 597 242 290 321 1110 303
1935_TCX 737 1887 998 2563 856 949 713 1060 2670 569
1925_TCX 634 1323 575 1661 594 562 421 1197 1796 595
1963_TCX 508 696 429 1030 355 556 365 585 1591 360
7098_TCX 418 1011 318 1574 354 353 179 471 1471 321
All of this code is wrapped around the following header in the RMarkdown environment: {r heatmaps, echo=FALSE, results="asis", message=FALSE}.
What I would like to achieve is the two heatmaps side-by-side with black boxes around each individual heat map (i.e. containing the title and legend of the heatmap as well).
If anyone could tell me how to do this, or either one individually it would be greatly appreciated.
Thanks!
I have a MATLAB polygon (below) which self intersects to make the zero volume part (which I will call a sliver) in the image (also below).
I am having a lot of trouble finding and removing the zero volume sliver.
Hope anyone can help. Thanks.
IMAGE:
enter image description here
Polygon:
newBoundary =
1 1
1 216
8 221
25 239
46 255
60 269
70 282
81 296
92 313
113 323
127 326
145 336
163 349
170 368
174 374
192 388
209 400
229 416
236 433
252 450
266 470
268 488
274 504
284 523
274 518
249 513
246 532
246 538
224 527
216 541
206 554
205 573
192 589
185 590
169 609
148 620
154 641
170 646
172 660
165 683
154 673
140 654
130 648
116 656
105 660
92 679
82 700
69 719
90 730
77 751
87 770
66 763
48 774
37 791
28 804
18 804
9 817
1 834
1 838
1 1049
131 1049
141 1044
164 1029
181 1014
193 1004
208 995
221 979
239 979
239 962
253 951
270 940
290 926
299 908
317 899
332 879
353 869
364 857
379 841
376 856
386 870
383 889
380 913
382 933
383 958
378 975
379 993
378 1016
374 1029
367 1040
366 1049
616 1049
615 1049
610 1037
615 1016
612 993
618 978
612 953
609 936
606 913
608 894
609 874
606 854
622 839
633 853
651 869
670 883
690 900
706 916
721 934
740 951
747 971
765 978
785 992
795 1007
816 1015
831 1028
848 1049
1049 1049
1049 781
1027 784
1003 780
997 798
974 812
973 796
964 780
947 765
930 773
922 760
926 736
917 722
898 713
879 719
883 704
886 679
873 663
853 670
847 649
841 627
839 607
835 621
814 631
794 620
778 604
761 588
744 601
729 592
716 577
705 556
698 537
689 517
698 502
711 486
726 486
730 463
743 446
760 439
764 420
784 404
802 388
816 373
837 366
846 344
866 337
881 324
901 314
912 299
926 283
946 272
951 253
970 242
977 225
998 223
1006 207
1025 192
1049 186
1049 1
789 1
792 6
778 20
774 37
762 49
753 70
736 79
715 90
695 100
672 91
668 109
663 131
668 153
651 156
657 168
648 170
633 153
612 150
613 127
610 114
613 97
615 74
613 54
612 36
613 21
620 1
380 1
379 9
377 34
371 56
379 78
387 98
379 117
376 136
384 151
372 156
350 164
332 174
318 168
327 150
309 139
299 129
281 127
290 108
275 107
261 90
236 76
235 60
219 52
206 35
187 22
168 4
156 1
1 1
Zero volume slivers may occur when there are three or more consecutive points aligned. In this particular case those were:
366 1049
616 1049
615 1049
since they fall on the same horizontal line.
If you want to automate this, you should check every group of three consecutive points to see if they fall on the same line. If they do, you need to discard the one in the middle, according to appearance order.
In this particular case, the one in the middle is:
616 1049
I am conducting Spearman's Correlation with two data sets with 300 objects. These are my variables and commands:
a = [1:300]
b = [1 2 5 11 9 7 24 10 31 23 3 40 6 17 14 20 16 12 33 46 70 37 87 43 98 26 59 58 77 100 35 42 78 80 243 36 33327 4 83 160 163 198 86 94 406 111 28 29 55 113 239 295 110 196 177 32679 229 342 305 300 254 96 210 514 167 172 232 190 117 32081 25 158 19333 241 82 149 159 66 178 24487 68 30 1016 725 266 391 638 348 320 681 242 319 228 381 408 442 202 369 471 821 191 426 8 270 211 2266 619 576 441 680 3431 1167 723 74 318 556 640 395 1059 579 614 212 325 437 323 687 373 599 26637 985 54 84 802 724 154 417 240 1120 818 2309 462 109 104 509 494 427 57 2475 549 396 419 123 580 79 225 1132 351 76 16859 596 862 315 470 992 257 120 409 751 832 285 1534 714 1665 1376 2129 678 416 721 209 31971 183 356 1346 1015 1003 188 1076 1634 608 1056 338 308 145 418 625 1313 121 2484 996 783 329 1185 697 157 1100 175 622 235 456 277 166 2700 1439 461 653 433 540 1191 234 774 1894 1004 741 1062 948 48 99 405 797 237 1104 2286 22620 1429 30672 1808 169 458 22 1115 10660 872 474 1063 88 1727 1017 1107 1398 1519 703 1092 1027 272 263 1152 1770 1099 507 385 2118 19356 1778 2458 410 2110 7522 17166 4065 15136 13294 10876 17174 2434 9898 5663 13594 10506 11552 15635 9322 3223 8949 12388 13216 13851 13852 6696 12177 4700 17199 2067 11110 15486 5664 6593 4701 527 8616 268]
[RHO,PVAL] = corr(b',a','Type', 'Spearman')
RHO =
0.6954
PVAL =
0
Out of the 5 comparisons I made with other data sets of 300 objects, only 1 returned significant P-values. Is there an explanation for this?
I tried a different data set and got a value that was not significant (PVAL > 0.05). I also displayed the answer in a long (15 digits) and exponential form and got 0.00000000000000e+000 using:
format longEng
I also checked with another statistics program that reported the p-value as < 0.0001. This means that the p-value is just really, really small.
I have a program that outputs the number of iterations and a test value, given inputs A1,A2,A3,A4.
I want to run through 5 values each of A1, A2, A3, A4, thus making 625 runs. In the process, I want to save the time elapsed for each run, the number of iterations, and test value in 3 separate matrices.
I have tried using 4 nested for loops, and made progress, but need some help on indexing the elements of the matrices. The iterator variables in the for loops don't match the indexing variables...
The code for the 4 nested loops is below:
m = logspace(-4,4,5);
n = logspace(0,8,5);
eltime = zeros(5,length(m)*length(m)*length(m));
for A1 = m
for A2 = m
for A3 = m
for A4 = n
tic
SmallMAX(A1,A2,A3,A4)
toc;
for i=1:numel(eltime)
for j = 1:length(n)
eltime(j,i) = toc;
end
end
end
end
end
end
The code for the main program is excerpted below:
function [k,test] = SmallMAX(A1,A2,A3,A4)
...
end
Thanks for any help.
In your case, the easiest way is to use A1, A2, A3 and A4 as counters instead of the actual values. This way you them to index the entries of eltime. We can then easily calculate the index in the second dimension with sub2ind and use A4 to index the first dimension of eltime. We need to adjust the arguments in SmallMAX as well.
Here is the code of the proposed method:
m = logspace(-4,4,5);
n = logspace(0,8,5);
eltime = zeros(length(n),length(m)*length(m)*length(m));
res_k = zeros(length(n),length(m)*length(m)*length(m)); % or zeros(size(eltime));
res_test = zeros(length(n),length(m)*length(m)*length(m)); % or zeros(size(eltime));
for A1 = 1:length(m)
for A2 = 1:length(m)
for A3 = 1:length(m)
for A4 = 1:length(n)
ind = sub2ind([length(m),length(m),length(m)],A3,A2,A1);
tic
[k,test] = SmallMAX(m(A1),m(A2),m(A3),n(A4));
eltime(A4,ind) = toc;
res_k(A4,ind) = k;
res_test(A4,ind) = test;
end
end
end
end
This is the order of the addressed entries of eltime:
eltime_order =
Columns 1 through 18
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86
2 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87
3 8 13 18 23 28 33 38 43 48 53 58 63 68 73 78 83 88
4 9 14 19 24 29 34 39 44 49 54 59 64 69 74 79 84 89
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Columns 19 through 36
91 96 101 106 111 116 121 126 131 136 141 146 151 156 161 166 171 176
92 97 102 107 112 117 122 127 132 137 142 147 152 157 162 167 172 177
93 98 103 108 113 118 123 128 133 138 143 148 153 158 163 168 173 178
94 99 104 109 114 119 124 129 134 139 144 149 154 159 164 169 174 179
95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180
Columns 37 through 54
181 186 191 196 201 206 211 216 221 226 231 236 241 246 251 256 261 266
182 187 192 197 202 207 212 217 222 227 232 237 242 247 252 257 262 267
183 188 193 198 203 208 213 218 223 228 233 238 243 248 253 258 263 268
184 189 194 199 204 209 214 219 224 229 234 239 244 249 254 259 264 269
185 190 195 200 205 210 215 220 225 230 235 240 245 250 255 260 265 270
Columns 55 through 72
271 276 281 286 291 296 301 306 311 316 321 326 331 336 341 346 351 356
272 277 282 287 292 297 302 307 312 317 322 327 332 337 342 347 352 357
273 278 283 288 293 298 303 308 313 318 323 328 333 338 343 348 353 358
274 279 284 289 294 299 304 309 314 319 324 329 334 339 344 349 354 359
275 280 285 290 295 300 305 310 315 320 325 330 335 340 345 350 355 360
Columns 73 through 90
361 366 371 376 381 386 391 396 401 406 411 416 421 426 431 436 441 446
362 367 372 377 382 387 392 397 402 407 412 417 422 427 432 437 442 447
363 368 373 378 383 388 393 398 403 408 413 418 423 428 433 438 443 448
364 369 374 379 384 389 394 399 404 409 414 419 424 429 434 439 444 449
365 370 375 380 385 390 395 400 405 410 415 420 425 430 435 440 445 450
Columns 91 through 108
451 456 461 466 471 476 481 486 491 496 501 506 511 516 521 526 531 536
452 457 462 467 472 477 482 487 492 497 502 507 512 517 522 527 532 537
453 458 463 468 473 478 483 488 493 498 503 508 513 518 523 528 533 538
454 459 464 469 474 479 484 489 494 499 504 509 514 519 524 529 534 539
455 460 465 470 475 480 485 490 495 500 505 510 515 520 525 530 535 540
Columns 109 through 125
541 546 551 556 561 566 571 576 581 586 591 596 601 606 611 616 621
542 547 552 557 562 567 572 577 582 587 592 597 602 607 612 617 622
543 548 553 558 563 568 573 578 583 588 593 598 603 608 613 618 623
544 549 554 559 564 569 574 579 584 589 594 599 604 609 614 619 624
545 550 555 560 565 570 575 580 585 590 595 600 605 610 615 620 625